In high-income countries, stroke incidence in the 21st century has increased or stagnated in young and middle-aged individuals. Assessing temporal trends in post-stroke outcomes is necessary to understand the impact of these changes in incidence. Embase and MEDLINE were searched from January 1, 1970, to October 30, 2024. Studies were included if they reported case fatality, 1-year or 5-year mortality, or stroke recurrence in patients aged up to 65 years. For studies reporting outcomes across multiple time periods, risk ratios (RR) were calculated comparing the earliest and latest periods and pooled using inverse-variance random-effects models. The annualised change in RR for case fatality was estimated using meta-regression. Twenty-two studies reported case fatality across 28 populations after all strokes or ischaemic stroke and intracerebral haemorrhage (14 population-based, 14 based on routinely collected data). The median (IQR) duration between compared time points was 9 (7-13.3) years. Case fatality decreased over time (RR 0.74, 95% CI 0.68-0.81), corresponding to a mean annual relative reduction of 3.2% (95% CI 1.4-5.0). Results were consistent by age, sex, study design, geographical region, and stroke subtype (6 studies; ischaemic stroke, RR 0.79, 95% CI 0.75-0.83; intracerebral haemorrhage, RR 0.77, 95% CI 0.69-0.86). Trends were similar before and after 2000, with continued improvement after 2010 compared with 2000-2009 (6 studies; RR 0.70, 95% CI 0.63-0.77). In addition, mortality decreased at 1 year (5 studies; RR 0.68, 95% CI 0.52-0.87) and at 5 years after stroke (4 studies; RR 0.65, 95% CI 0.45-0.95). In contrast, the 1-year cumulative incidence of recurrent stroke was unchanged after 2000 (3 studies; RR 1.04, 95% CI 0.78-1.39). Early case fatality and long-term survival after stroke have progressively improved since the 1970s in high-income countries among young and middle-aged individuals, but stroke recurrence rates may have stagnated in the past two decades.

All-cause midlife mortality rates have been increasing since 2010 in the United States. Using data from 1970 to 2010, this study investigates the association between county-level changes in economic, social, and employment sectors and changes in midlife mortality rates the occurred between 2010 and 2018. The study employs a novel approach to analyze temporal trends. County-level mortality data for 2009-2019 were obtained from the Centers for Disease Control and Prevention (CDC), while decennial data for 19 indicators-covering socioeconomic conditions, social factors, and employment sectors-were obtained from IPUMS NHGIS time series tables and the US Bureau of Economic Analysis, Economic Profile by County. Data were examined for 3,069 (97.6%) of the 3,143 U.S. counties and county equivalents. Absolute changes in county characteristics were measured over ten possible comparison periods: single decades, two decades, three decades, and four decades. LASSO regression was used to identify significant predictors and assess their impact over multiple time periods. While changes in some county characteristics (e.g., households headed by single mothers, employment in certain sectors, college education, and labor force participation), tend to be associated with higher or lower mortality risk; in many cases the strength and direction of observed associations differed depending on time period, place, and race. These results reveal the importance of historical and contextual factors in understanding mortality trends and highlight the complex interplay between social determinants and health outcomes. This study provides insights into the drivers of midlife mortality and a nuanced look at the temporal dynamics and geographic variations in mortality trends. By identifying critical time periods and specific predictors associated with mortality changes, the study informs policy and public health efforts aimed at reducing mortality disparities and improving population health outcomes.

Abstract—Cloud computing costs have become a critical concern for organizations deploying workloads on public cloud platforms. Manual cost optimization approaches are inherently limited by the combinatorial complexity of resource allocation decisions across multiple services and time periods. This paper presents a research-grade Cloud Cost Optimization System built for Microsoft Azure that employs two nature-inspired evolutionary algorithms — the Genetic Algorithm (GA) and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) — to automate and mathematically validate cost reduction strategies while preserving service performance. The system is implemented as a full-stack Python Flask web application that ingests real-world Azure billing export data, performs statistical anomaly detection using Z-score analysis, conducts linear regression trend forecasting, generates rule-based recommendations, and runs both single-objective (GA) and multi-objective (NSGA-II) evolutionary optimization. Experimental results on a 12-month dataset of 489 records across 10 Azure services demonstrate consistent cost reductions of 20–50% while maintaining service performance above user-specified thresholds. NSGA-II further produces a Pareto-optimal front of 20–50 non-dominated solutions per run, enabling decision-makers to select cost-performance trade-offs aligned with their specific business priorities. The system introduces a dynamic optimization engine that allows users to compare algorithm outputs on both the original dataset and custom-defined service costs simultaneously, enabling real-world deployment scenarios beyond the training data. This work demonstrates that evolutionary computation techniques are highly effective for cloud financial management (FinOps) and provides a reproducible, open-architecture framework for researchers and practitioners to further extend and validate cloud cost optimization techniques. Keywords—Cloud Computing, Cloud Cost Optimization, FinOps, Genetic Algorithm (GA), NSGA-II, Multi-Objective Optimization, Evolutionary Algorithms, Resource Allocation, Waste Detection, Z-score Analysis, Linear Regression, Cost Forecasting, Microsoft Azure, Cloud Analytics, Performance Optimization

Private wealth, as well as its distribution and intergenerational transmission have become much-debated issues. However, existing evidence remains fragmented, context-dependent, and sometimes contested. This data descriptor introduces the Graduate Center (GC) Wealth Project data warehouse, a collection of databasescovering multiple countries and time periods, designed to address these challenges. The data warehouse consolidates most existing evidence on private wealth and undertakes a significant data harmonization effort. We supplement each data point with extensive metadata on methodology. The data warehouse features an extensive collection of information on the household wealth levels and balance sheets, along with distributional statistics from a wide range of sources. Moreover, it draws together data on wealth transfer tax revenues, and tax features such as rates, exemption thresholds, and tax schedules. The broad range of data sources in the warehouse allows users to assess the degree of heterogeneity of estimates, and how methodological choices affect measurement outcomes. The new data series and policy indicators also allow extending quantitative analysis of wealth and public policy.

Large electronic datasets do not always correctly identify women (defined as individuals whose sex assigned at birth was female, although we acknowledge that biological sex does not always correlate with gender identity) experiencing menopause or their symptoms, making it challenging to generate robust evidence on the burden of menopause. This study aimed to construct and characterize cohorts of women experiencing menopause to explore differences in their identification based on the cohort criteria and to provide recommendations for defining cohorts of individuals experiencing menopause in real-world data. This was a retrospective, multi-country cohort study using databases that included a total of ~240 million individuals from five countries: three observational electronic health record databases from France, Germany, and the UK; and two administrative claims databases from Japan and the USA. Demographic, comorbidity, and concomitant medication covariates were used to characterize the study target cohorts at multiple time periods relative to the index event. Counts were calculated for the number of women (as identified in the databases) in each database that matched the target cohort criteria. Cohorts that included a more specific cohort definition of women aged 40–65 with a probable menopause diagnosis with or without symptom criteria produced age distributions that best matched those expected for women experiencing menopause, along with the highest VMS incidence rates. The prevalence of comorbidities varied between databases and cohorts. However, generally, the most common comorbidities were anxiety, depression, hypertension, hypothyroidism, and osteoarthritis. The findings provide evidence to support the development of a reproducible definition of menopause and menopause symptoms, which can be used for future analysis of observational healthcare databases.

ABSTRACT Although several past studies have addressed sustainable transportation issues within the supply chain, many critical real-world factors such as time-dependent vehicle speeds, actual route distances, and load-dependent engine efficiency have not been sufficiently addressed. In order to address these limitations, present study proposes a sustainable transportation network that simultaneously optimizes economic, environmental, and social objectives. A capacitated vehicle routing and scheduling problem is formulated, incorporating heterogeneous vehicles, hard time windows, and multiple time periods. An improved Ant Colony Optimization algorithm is developed with 2-optimal local search for enhanced route quality and convergence. Pareto-optimal solutions are ranked using the technique for order preference by similarity to ideal solution. Partial rank correlation coefficient and regression analysis are conducted to interpret variable influence, offering insights into input-output relationships. A real-world case study from a rural logistics company demonstrates the practical applicability and effectiveness of the proposed framework in solving complex sustainable transportation problems.

ABSTRACT Significant increases in metro ridership during holidays place substantial pressure on metro operations and may pose safety risks. Understanding the spatiotemporal characteristics of holiday ridership and its differences from nonholiday periods is, therefore, essential. Using smartcard data from Wuhan, China, this study compares metro travel patterns during International Workers' Day with those on weekdays and weekends across multiple time periods and applies a combined geographically weighted regression and extreme gradient boosting (GWR-XGBoost) model. Results show that average daily ridership during holidays is 1.16 times that of weekends and 1.23 times that of weekdays, accompanied by more dispersed spatial distributions and longer travel times. The influence and relative importance of built environment factors exhibit pronounced temporal and spatial heterogeneity. Unlike non-holiday periods, holiday ridership is concentrated around tourist attractions and closely related to cultural facilities and companies. These findings offer valuable insights for metro operation management and travel safety during holidays.

Poverty persists as a pervasive global challenge, posing important barriers to the advancement of both social equity and economic development. Against this backdrop, this study focuses on rural poverty in China, with the specific aim of investigating its dynamics through a spatiotemporal analysis of impoverished counties across multiple time periods. To achieve this goal, the DBSCAN (density-based spatial clustering of applications with noise) spatial clustering algorithm is first utilized to identify the spatial clusters of poverty and associated risk areas; subsequently, the MGWR (multiscale geographically weighted regression) model is used to evaluate the spatial characteristics of poverty-causing factors and unpack the underlying mechanisms driving poverty. The study identifies 663, 832, and 52 state-level impoverished counties in China in 2001, 2016, and 2021, respectively. Moreover, the counties are categorized into 6 types based on the principal factors causing poverty, namely, frontier terrain constraints, infrastructure limitations, geological disaster vulnerabilities, minority aggregation industry constraints, underdeveloped old revolutionary areas, and ecological fragility. Among these factors, topographic relief and healthcare institutions exhibit a negative correlation with poverty, while average slope and nighttime light index display a positive correlation. Furthermore, location conditions, farmland production potential, vegetation index, residents’ deposit balance, and local government revenue demonstrate a 2-way influence on gross domestic product per capita in impoverished counties. This study offers targeted and actionable insights to local governments, supporting the localized and effective implementation of targeted poverty alleviation strategies.

Rising seroprevalence of Borrelia spp. in free roaming felines in the easternTennessee valley.

Malaria is a major public health challenge in sub-Saharan Africa, with transmission patterns that vary significantly across space and time due to environmental, socioeconomic, and epidemiological factors. These variations complicate efforts to design effective and targeted interventions, making it crucial to understand the dynamics of disease spread. This study employed Bayesian spatio-temporal random effects modeling framework to analyze malaria incidence and mortality ratio across Kenya. The approach incorporated spatial and temporal dependencies to provide a detailed understanding of malaria incidence and mortality risk patterns. Spatial random effects were modeled using conditional autoregressive (CAR) priors to account for correlations among neighboring counties, while temporal dependence was captured using autoregressive processes of order two (AR2), reflecting trends over multiple time periods. An evaluation was on the performance of Spatio-Temporal Poisson Linear Trend Model (STPLM), Spatio-Temporal Poisson ANOVA Model (STPAM), Spatio-Temporal Poisson Separable Model (STPSM) and Poisson Temporal Model for Spatio-Temporal Effects (PTSTN)using the Deviance Information Criterion (DIC), the effective number of parameters (p.d) and the Log Marginal Pseudo-Likelihood (LMPL). The Spatio-Temporal Poisson ANOVA Model (STPAM) was found as the best Poissson Spatial-Temporal Model and was used to develop a multivariate spatio-temporal model for the joint modeling of malaria incidence and mortality. Using the developed model, the study identified significant spatial clustering of malaria, with persistent high-risk zones in western and coastal counties. Temporal trends indicated an overall decline in transmission, though progress was uneven across counties, reflecting differences in intervention coverage, healthcare access, and local epidemiology. These findings underscored the value of multivariate spatio-temporal modeling of malaria incidence and mortality for guiding malaria control strategies. This study thus demonstrates that Bayesian Spatial-Temporal modeling is essential for understanding heterogeneous malaria incidence and mortality risk and informing strategies aimed at reducing disease burden and advancing toward malaria elimination in Kenya.

Analysis of Echinococcus multilocularis complete mitogenomes: evidence for the historical spread from Central Asia to the Northern Hemisphere.

Day-ahead electricity auctions allow market participants to trade power for delivery the following day. In Europe, these auctions are designed as combinatorial auctions, enabling agents to submit package bids (“block bids”) that span multiple time periods rather than bidding separately for each hour. However, power exchanges impose limits on the number of package bids an agent can submit, creating a complex decision problem: Which packages should an agent bid on to best represent their preferences? In “Package Bids in Combinatorial Electricity Auctions: Selection, Welfare Losses, and Alternatives,” Hübner and Hug study this selection problem and propose decision-support algorithms that optimize bid choice under uncertainty. They provide theoretical bounds on welfare loss due to bid limits and validate their methods with simulations involving generators, storage systems, and flexible demand. Their findings offer actionable insights for both auctioneers and bidders.

In this paper, the generalized quadratic assignment problem (GQAP) is extended to consider multiple time periods and is called the dynamic GQAP (DGQAP). This problem considers assigning a set of facilities to a set of locations for multiple periods in the planning horizon such that the sum of the transportation, assignment, and reassignment costs is minimized. The facilities may have different space requirements (i.e., unequal areas), and the capacities of the locations may vary during a multi-period planning horizon. Also, multiple facilities may be assigned to each location during each period without violating the capacities of the locations. This research was motivated by the problem of assigning multiple facilities (e.g., equipment) to locations during outages at electric power plants. This paper presents mathematical models, construction algorithms, and two simulated annealing (SA) heuristics for solving the DGQAP problem. The first SA heuristic (SAI) is a direct adaptation of SA to the DGQAP, and the second SA heuristic (SAII) is the same as SAI with a look-ahead/look-back search strategy. In computational experiments, the proposed heuristics are first compared to an exact method on a generated data set of smaller instances (data set 1). Then the proposed heuristics are compared on a generated data set of larger instances (data set 2). For data set 1, the proposed heuristics outperformed a commercial solver (CPLEX) in terms of solution quality and computational time. SAI obtained the best solutions for all the instances, while SAII obtained the best solution for all but one instance. However, for data set 2, SAII obtained the best solution for nineteen of the twenty-four instances, while SAI obtained five of the best solutions. The results highlight the effectiveness and efficiency of the proposed heuristics, particularly SAII, for solving the DGQAP.

Vaccine hesitancy is a significant 21st Century public health challenge, with vaccines being increasingly politicized in public and government discourse and policymaking, particularly since the introduction of COVID-19 vaccines. We evaluate the potential roles of national political and policy context for individuals' COVID-19 vaccine hesitancy across 27 European Union countries at two timepoints of the pandemic-assessing the extent to which national-level populism and government trustworthiness are associated with individuals' COVID-19 vaccination attitudes and receipt (actual or intended)-directly and indirectly via COVID-19-related policies. We analyzed May 2021 and February 2022 Eurobarometer survey data merged with nation-level populist parties' vote share, Transparency International's Corruption Index, the COVID-19 Policy Stringency Index, plus nation- and respondent-level controls. Increased levels of government trustworthiness and COVID-19 policies, but not populism, were each associated with more positive COVID-19 vaccine attitudes (though not substantively) and, respectively, lower odds of anti-vaccine and uncertain (vs. pro-vaccine) COVID-19 vaccination receipt/intentions. These associations were limited to only the May 2021 period that followed government and scientific scrutiny of vaccine safety signals. No evidence indicated that attitudes and receipt/intention were indirectly associated with populism and trustworthiness via COVID-19 policies. Our findings indicate that governments viewed as more trustworthy have citizens more accepting of government-run vaccination programs. These pandemic era analyses corroborate prior pre-COVID-19 research on nation-level evaluations of populism and vaccine acceptance. Our study highlights the value of multilevel approaches and multiple time periods and measures for advancing understanding of how political context may contribute to vaccination decision-making.

Abstract Background Frailty is prevalent in older gastric cancer patients, seriously affecting prognosis. This study aimed to estimate the effect of hypothetical interventions on frailty heterogeneous trajectories (FHT) to design targeted intervention programs. Methods Data from a longitudinal follow-up study of 381 gastric cancer patients ≥60 years were collected at admission, discharge, and 1, 3, 6, and 12 months post-surgery. The parametric g-formula estimated the effects of single and joint hypothetical interventions on FHT risk across physical, psychological, familial, and social factors. Results The observed FHT risk was 43.57%. Single intervention on nutrition most effectively reduced FHT risk (RR = 0.790, 95% CI: 0.692-0.974), followed by family cohesion (RR = 0.810) and social objective support (RR = 0.822). The “all-factors” joint intervention reduced risk by 31.26% (RR = 0.323, 95% CI: 0.208-0.685). An evidence-based intervention program was designed with good precision and scientificity. Conclusions Based on optimal intervention combinations determined from parametric g-formula, we designed a comprehensive FHT intervention program covering multiple time periods, scenarios and factors, providing theoretical and practical guidance for frailty prevention in older gastric cancer patients.

This research aims to evaluate: (1) adventure-based learning designs, (2) the success of adventure-based learning programs, and (3) unplanned but achievable goals by adventure-based learning programs. This research is evaluation research (evaluation research) with a qualitative approach; the evaluation model used is a goal-free evaluation model (Goal Free Evaluation). The population and sample in this study were all participants in adventure-based learning at Jogja Adventure Kids from December 2022 to June 2023, totaling 13 children. Additionally, there were nine parents of students and one manager from Jogja Adventure Kids. The location of this research is in an open/wild space organized by Jogja Adventure Kids. Data collection techniques encompass both primary data, such as direct interviews and observations, and secondary data, including document reviews. This research instrument consists of an observation guide, an interview guide, field notes, and documentation. Data validity was achieved through triangulation with multiple sources, methods, and time periods. The data analysis techniques used are display, reduction and drawing conclusions. The results of the research show that (1) evaluation of adventure-based learning design includes the material provided and learning methods, (2) evaluation of success contains good things and bad things, and (3) evaluation of unplanned but achievable goals includes being a vehicle for channeling energy excess and becoming a health investment.

ABSTRACT Data that span multiple units and time periods allow controlling for time‐invariant heterogeneity correlated with the covariates. While researchers can do this in different ways, the fixed effects estimator—also known as the within estimator, and equivalent to the least squares dummy variable approach—has become the default choice. But when time‐invariant attributes are not invariant to time—that is, when they are not invariant to the length of the panel—the fixed effects estimator can be considerably biased as researchers incorporate additional time periods. We show that, in finite samples, first‐differencing and novel rolling estimators can offer researchers a practical alternative to the fixed effects estimator in this case. These estimators are simple to implement and can significantly reduce bias relative to the fixed effects estimator under certain data‐generating processes. Most importantly, researchers should always provide results from multiple estimators. We illustrate this with simulations and four replications.

Understanding the mechanisms that drive the trade-off intensity of ecosystem services in basins is critical to achieving sustainable regional development. First, this study quantifies ecosystem services using the InVEST model and then calculates the trade-off intensity using the root mean square error (RMSE). Second, a Bayesian network model is combined to analyze the dynamics of the drivers of trade-off intensity over multiple time periods. Finally, a probabilistic model of Bayesian networks is used to simulate areas within the basin that are more likely to have high trade-off intensities. The results show a significant increase in grain production but a decrease in water conservation, water purification, habitat quality and carbon storage. Bayesian inference identified precipitation, the proportion of cropland and the NDVI as dominant drivers. Spatial optimization simulations highlighted Heze and Zaozhuang city as priority regions, suggesting integrated land–water management strategies to reconcile agricultural and ecological objectives.

ABSTRACT Aim The effects of rivers as barriers for species dispersal can be expected to vary according to both species traits and the characteristics of rivers themselves. In this context, our aim was to investigate dissimilarities in species composition across opposite banks of two major Amazonian rivers, the Juruá and the Tapajós, and relate them to each river's characteristics and history. Location The study was conducted in upland forests on opposite banks of two contrasting Amazonian rivers: the Juruá and the Tapajós. The Juruá River, a highly meandering and sediment‐rich river, drains the Solimões sedimentary basin. In contrast, the Tapajós River is an entrenched, low‐sediment river flowing through the cratonic region of the ancient Brazilian shield. These rivers represent distinct geomorphological and geological settings, allowing us to assess their differential effects on bird species compositions. Time Period Data were collected over multiple time periods, spanning several decades. The earliest surveys were conducted in the 1990s. Additional data were collected in 2012 and 2013, followed by a more recent sampling effort in 2022. Major Taxa Studied Upland forest bird assemblages, including species with different ecological traits, such as varying body sizes, diets and dispersal abilities, ensure an evaluation of how species characteristics interact with riverine features in shaping bird community structure. Methods We used standardised bird surveys to assess taxon (genus, species and subspecies) composition of upland bird assemblages on both sides of each river. Occupancy and abundance data were obtained using mist‐net lines. Differences in composition were analysed using statistical methods to assess community dissimilarity and test for significant differences between opposite margins. We assessed the relative contributions of river basins, geographical distances and environmental variables in explaining dissimilarities in bird taxon composition across the Juruá and Tapajós rivers. Results We found significant differences in bird assemblages between the opposite margins of the stable Tapajós, but not between the margins of the more dynamic Juruá. These findings suggest that the Tapajós, with its entrenched channel and long‐term geological stability, acts as a stronger biogeographic barrier than the Juruá, which has a broader floodplain but a more dynamic and meandering course. The less pronounced barrier effect of the Juruá is likely due to its dynamic current and historical geomorphological processes, which facilitate species dispersal across the river and its floodplain. Main Conclusions Our findings are consistent with the notion that geomorphological history and fluviomorphic stability can influence the strength of Amazonian rivers as biogeographic barriers, but broader replication is needed to generalise this pattern across the Amazon basin. Rivers with a more dynamic and shifting course, like the Juruá, appear to exert weaker dispersal limitations, whereas stable rivers, like the Tapajós, create more pronounced differences in species composition. This supports the notion that rivers experiencing a more dynamic history exert a weaker geographic barrier effect, and that geomorphological setting and landscape history are important determinants of the effect of Amazonian rivers on the dispersal and diversification of Amazonian vertebrate taxa.

The extent to which human adaptations have persisted throughout history despite strong eroding demographic events such as admixture, genetic drift, and fluctuations in selection pressures remains unknown. Understanding which loci are particularly resilient to such forces may shed light on the traits that were important for humans throughout multiple time periods. Yet, detecting ancient selection events is challenging from modern and ancient DNA due to the data and/or signal being severely degraded. Here we use a domain-adaptive neural network (DANN) trained on simulated data and applied to ancient and modern DNA for sweep detection. We show that the DANN can account for simulation misspecification, or discrepancies between the simulations and real aDNA, thereby improving the ability to detect sweeps in real data. Application of the DANN to more than 800 ancient and modern human genomes spanning the last 7000 years recovered 16 known sweeps at loci including LCT, HLA, KITLG, and OCA2/HERC2, and revealed 32 novel sweeps. All identified sweeps were classified as hard, consistent with historically low population sizes. While some sweeps were lost over time, 14 sweeps at loci involved in a range of functions including neuronal, reproductive, pigmentation, and signaling traits were found to persist from the most ancient time periods into the most recent time periods. Notably, the same top haplotype remained at high frequency across time at 9 of these 14 sweeps. Together, these results indicate that hard sweeps predominated in ancient human history and that several ancient selective events were resilient to strong admixture events and experienced sustained selective pressures.